KEYWORDS: Neural networks, Signal generators, Data modeling, Gold, Embedded systems, Diffusers, Detection and tracking algorithms, Control systems, Brain
We propose a general neural-network based learning framework to solve highly ill-posed problems to predict a system’s forward and backward response function. Such an approach has applications in target-oriented system’s control in fields such as, optics, neuroscience and robotics. The proposed method is able to find the appropriate continuous space input of a system that results in a desired output, despite the input-output relation being nonlinear, the system being time-variant and\or with incomplete measurements of the systems variables and lack of labeled data required for supervise learning.
We propose an imaging method for controlling the output of scattering media such as multimode fibers using machine learning. Arbitrary images can be projected with amplitude-only calibration (no phase measurement) and fidelities on par with conventional full-measurement methods.
Image delivery through multimode fibers (MMFs) suffers from modal scrambling which results in a speckle pattern at the fiber output. In this work, we use Deep Neural Networks (DNNs) for recovery and/or classification of the input image from the intensity-only images of the speckle patterns at the distal end of the fiber. We train the DNNs using 16,000 images of handwritten digits of the MNIST database and we test the accuracy of classification and reconstruction on another 2,000 new digits. Very positive results and robustness were observed for up to 1 km long MMF showing 90% reconstruction fidelity. The classification accuracy of the system for different inputs (phase-only, amplitude-only, hologram intensity etc.) to the DNN classifier was also tested.
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